In-class Excercise 8: Hedonic Pricing Model
Import data
The longitude and latitude in condo_resale dataset is in decimal degree, highly likely to adopt WGS84 coordinate system. When we convert condo_resale into sf, we first indicate that it is using coordinate system WGS84 (EPSG:4326), then project into Singapore coordinate system SVY21 (EPSG:3414).
Show the code
Reading layer `MP14_SUBZONE_WEB_PL' from data source
`/Users/tangtang/Desktop/IS415 Geospatial Analytics and Applications/practice/is415gaa/data/geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21Rows: 1436 Columns: 23
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (23): LATITUDE, LONGITUDE, POSTCODE, SELLING_PRICE, AREA_SQM, AGE, PROX_...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Exploratory Data Analysis
Visualising relationships between variables
This function ggcorrmat() from ggstatsmodel package provides information than the regular corrmat() used in Hands-on Excercise 8.
This quick plot function will identify both significant and insignificant variables.
Hedonic Pricing Model - Multivariate Linear regression
When porting into Shiny app, the independent variables are to be joined with + instead of ,.
Show the code
condo.mlr <- lm(formula = SELLING_PRICE ~ AREA_SQM + AGE +
PROX_CBD + PROX_CHILDCARE + PROX_ELDERLYCARE +
PROX_URA_GROWTH_AREA + PROX_HAWKER_MARKET + PROX_KINDERGARTEN +
PROX_MRT + PROX_PARK + PROX_PRIMARY_SCH +
PROX_TOP_PRIMARY_SCH + PROX_SHOPPING_MALL + PROX_SUPERMARKET +
PROX_BUS_STOP + NO_Of_UNITS + FAMILY_FRIENDLY + FREEHOLD,
data=condo_resale.sf)
summary(condo.mlr)
Call:
lm(formula = SELLING_PRICE ~ AREA_SQM + AGE + PROX_CBD + PROX_CHILDCARE +
PROX_ELDERLYCARE + PROX_URA_GROWTH_AREA + PROX_HAWKER_MARKET +
PROX_KINDERGARTEN + PROX_MRT + PROX_PARK + PROX_PRIMARY_SCH +
PROX_TOP_PRIMARY_SCH + PROX_SHOPPING_MALL + PROX_SUPERMARKET +
PROX_BUS_STOP + NO_Of_UNITS + FAMILY_FRIENDLY + FREEHOLD,
data = condo_resale.sf)
Residuals:
Min 1Q Median 3Q Max
-3475964 -293923 -23069 241043 12260381
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 481728.40 121441.01 3.967 7.65e-05 ***
AREA_SQM 12708.32 369.59 34.385 < 2e-16 ***
AGE -24440.82 2763.16 -8.845 < 2e-16 ***
PROX_CBD -78669.78 6768.97 -11.622 < 2e-16 ***
PROX_CHILDCARE -351617.91 109467.25 -3.212 0.00135 **
PROX_ELDERLYCARE 171029.42 42110.51 4.061 5.14e-05 ***
PROX_URA_GROWTH_AREA 38474.53 12523.57 3.072 0.00217 **
PROX_HAWKER_MARKET 23746.10 29299.76 0.810 0.41782
PROX_KINDERGARTEN 147468.99 82668.87 1.784 0.07466 .
PROX_MRT -314599.68 57947.44 -5.429 6.66e-08 ***
PROX_PARK 563280.50 66551.68 8.464 < 2e-16 ***
PROX_PRIMARY_SCH 180186.08 65237.95 2.762 0.00582 **
PROX_TOP_PRIMARY_SCH 2280.04 20410.43 0.112 0.91107
PROX_SHOPPING_MALL -206604.06 42840.60 -4.823 1.57e-06 ***
PROX_SUPERMARKET -44991.80 77082.64 -0.584 0.55953
PROX_BUS_STOP 683121.35 138353.28 4.938 8.85e-07 ***
NO_Of_UNITS -231.18 89.03 -2.597 0.00951 **
FAMILY_FRIENDLY 140340.77 47020.55 2.985 0.00289 **
FREEHOLD 359913.01 49220.22 7.312 4.38e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 755800 on 1417 degrees of freedom
Multiple R-squared: 0.6518, Adjusted R-squared: 0.6474
F-statistic: 147.4 on 18 and 1417 DF, p-value: < 2.2e-16The function tbl_regression() from gtsummary would create a tidy HTML summary table. In this excercise, we are including more model statistics into
Visualising model parameter
Use function ggcoefstats() from ggstatsmodel
This includes beta, t statistics and p-value for each variable, while indicating confidence interval with mean in form of line with dot.
When line/confidence interval is shorter, we are more confident (?)
Fixed bandwidth
Show the code
bw.fixed <- bw.gwr(formula = SELLING_PRICE ~ AREA_SQM + AGE +
PROX_CBD + PROX_CHILDCARE + PROX_ELDERLYCARE +
PROX_URA_GROWTH_AREA + PROX_HAWKER_MARKET + PROX_KINDERGARTEN +
PROX_MRT + PROX_PARK + PROX_PRIMARY_SCH +
PROX_TOP_PRIMARY_SCH + PROX_SHOPPING_MALL + PROX_SUPERMARKET +
PROX_BUS_STOP + NO_Of_UNITS + FAMILY_FRIENDLY + FREEHOLD,
data=condo_resale.sp,
approach = "CV",
kernel = "gaussian",
adaptive = FALSE,
longlat = FALSE)Fixed bandwidth: 17660.96 CV score: 8.235467e+14
Fixed bandwidth: 10917.26 CV score: 7.902384e+14
Fixed bandwidth: 6749.419 CV score: 7.152539e+14
Fixed bandwidth: 4173.553 CV score: 6.182116e+14
Fixed bandwidth: 2581.58 CV score: 5.257275e+14
Fixed bandwidth: 1597.687 CV score: 4.748442e+14
Fixed bandwidth: 989.6077 CV score: 5.095011e+14
Fixed bandwidth: 1973.501 CV score: 4.85724e+14
Fixed bandwidth: 1365.421 CV score: 4.766341e+14
Fixed bandwidth: 1741.235 CV score: 4.772231e+14
Fixed bandwidth: 1508.969 CV score: 4.745788e+14
Fixed bandwidth: 1454.139 CV score: 4.749631e+14
Fixed bandwidth: 1542.857 CV score: 4.7456e+14
Fixed bandwidth: 1563.8 CV score: 4.746245e+14
Fixed bandwidth: 1529.913 CV score: 4.745487e+14
Fixed bandwidth: 1521.913 CV score: 4.745531e+14
Fixed bandwidth: 1534.857 CV score: 4.745504e+14
Fixed bandwidth: 1526.857 CV score: 4.745494e+14
Fixed bandwidth: 1531.801 CV score: 4.74549e+14
Fixed bandwidth: 1528.746 CV score: 4.745488e+14
Fixed bandwidth: 1530.634 CV score: 4.745488e+14
Fixed bandwidth: 1529.467 CV score: 4.745487e+14
Fixed bandwidth: 1530.188 CV score: 4.745487e+14
Fixed bandwidth: 1529.743 CV score: 4.745487e+14
Fixed bandwidth: 1530.018 CV score: 4.745487e+14
Fixed bandwidth: 1529.848 CV score: 4.745487e+14
Fixed bandwidth: 1529.808 CV score: 4.745487e+14
Fixed bandwidth: 1529.873 CV score: 4.745487e+14
Fixed bandwidth: 1529.888 CV score: 4.745487e+14
Fixed bandwidth: 1529.863 CV score: 4.745487e+14
Fixed bandwidth: 1529.879 CV score: 4.745487e+14
Fixed bandwidth: 1529.869 CV score: 4.745487e+14
Fixed bandwidth: 1529.875 CV score: 4.745487e+14
Fixed bandwidth: 1529.871 CV score: 4.745487e+14
Fixed bandwidth: 1529.87 CV score: 4.745487e+14
Fixed bandwidth: 1529.87 CV score: 4.745487e+14
Fixed bandwidth: 1529.871 CV score: 4.745487e+14
Fixed bandwidth: 1529.87 CV score: 4.745487e+14
Fixed bandwidth: 1529.871 CV score: 4.745487e+14
Fixed bandwidth: 1529.87 CV score: 4.745487e+14
Fixed bandwidth: 1529.87 CV score: 4.745487e+14
Fixed bandwidth: 1529.87 CV score: 4.745487e+14 By default 2 models are built, one considering spatial relationship and the other without spatial relationship. Comparing the adjusted R-square (higher → better) to see if having spatial relationship will improve the model.
Compare AICc for performance of individual madels (higher → better).
Show the code
gwr.fixed <- gwr.basic(formula = SELLING_PRICE ~ AREA_SQM + AGE +
PROX_CBD + PROX_CHILDCARE + PROX_ELDERLYCARE +
PROX_URA_GROWTH_AREA + PROX_HAWKER_MARKET + PROX_KINDERGARTEN +
PROX_MRT + PROX_PARK + PROX_PRIMARY_SCH +
PROX_TOP_PRIMARY_SCH + PROX_SHOPPING_MALL + PROX_SUPERMARKET +
PROX_BUS_STOP + NO_Of_UNITS + FAMILY_FRIENDLY + FREEHOLD,
data = condo_resale.sp,
bw = bw.fixed,
kernel = "gaussian",
longlat = FALSE)
gwr.fixed ***********************************************************************
* Package GWmodel *
***********************************************************************
Program starts at: 2024-03-11 11:27:57.267851
Call:
gwr.basic(formula = SELLING_PRICE ~ AREA_SQM + AGE + PROX_CBD +
PROX_CHILDCARE + PROX_ELDERLYCARE + PROX_URA_GROWTH_AREA +
PROX_HAWKER_MARKET + PROX_KINDERGARTEN + PROX_MRT + PROX_PARK +
PROX_PRIMARY_SCH + PROX_TOP_PRIMARY_SCH + PROX_SHOPPING_MALL +
PROX_SUPERMARKET + PROX_BUS_STOP + NO_Of_UNITS + FAMILY_FRIENDLY +
FREEHOLD, data = condo_resale.sp, bw = bw.fixed, kernel = "gaussian",
longlat = FALSE)
Dependent (y) variable: SELLING_PRICE
Independent variables: AREA_SQM AGE PROX_CBD PROX_CHILDCARE PROX_ELDERLYCARE PROX_URA_GROWTH_AREA PROX_HAWKER_MARKET PROX_KINDERGARTEN PROX_MRT PROX_PARK PROX_PRIMARY_SCH PROX_TOP_PRIMARY_SCH PROX_SHOPPING_MALL PROX_SUPERMARKET PROX_BUS_STOP NO_Of_UNITS FAMILY_FRIENDLY FREEHOLD
Number of data points: 1436
***********************************************************************
* Results of Global Regression *
***********************************************************************
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-3475964 -293923 -23069 241043 12260381
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 481728.40 121441.01 3.967 7.65e-05 ***
AREA_SQM 12708.32 369.59 34.385 < 2e-16 ***
AGE -24440.82 2763.16 -8.845 < 2e-16 ***
PROX_CBD -78669.78 6768.97 -11.622 < 2e-16 ***
PROX_CHILDCARE -351617.91 109467.25 -3.212 0.00135 **
PROX_ELDERLYCARE 171029.42 42110.51 4.061 5.14e-05 ***
PROX_URA_GROWTH_AREA 38474.53 12523.57 3.072 0.00217 **
PROX_HAWKER_MARKET 23746.10 29299.76 0.810 0.41782
PROX_KINDERGARTEN 147468.99 82668.87 1.784 0.07466 .
PROX_MRT -314599.68 57947.44 -5.429 6.66e-08 ***
PROX_PARK 563280.50 66551.68 8.464 < 2e-16 ***
PROX_PRIMARY_SCH 180186.08 65237.95 2.762 0.00582 **
PROX_TOP_PRIMARY_SCH 2280.04 20410.43 0.112 0.91107
PROX_SHOPPING_MALL -206604.06 42840.60 -4.823 1.57e-06 ***
PROX_SUPERMARKET -44991.80 77082.64 -0.584 0.55953
PROX_BUS_STOP 683121.35 138353.28 4.938 8.85e-07 ***
NO_Of_UNITS -231.18 89.03 -2.597 0.00951 **
FAMILY_FRIENDLY 140340.77 47020.55 2.985 0.00289 **
FREEHOLD 359913.01 49220.22 7.312 4.38e-13 ***
---Significance stars
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 755800 on 1417 degrees of freedom
Multiple R-squared: 0.6518
Adjusted R-squared: 0.6474
F-statistic: 147.4 on 18 and 1417 DF, p-value: < 2.2e-16
***Extra Diagnostic information
Residual sum of squares: 8.094732e+14
Sigma(hat): 751322.9
AIC: 42970.18
AICc: 42970.77
BIC: 41784.96
***********************************************************************
* Results of Geographically Weighted Regression *
***********************************************************************
*********************Model calibration information*********************
Kernel function: gaussian
Fixed bandwidth: 1529.87
Regression points: the same locations as observations are used.
Distance metric: Euclidean distance metric is used.
****************Summary of GWR coefficient estimates:******************
Min. 1st Qu. Median 3rd Qu.
Intercept -1.7294e+06 5.0686e+05 1.2459e+06 2.0286e+06
AREA_SQM 2.7257e+03 5.4383e+03 7.8679e+03 1.2293e+04
AGE -8.0810e+04 -2.5075e+04 -1.2338e+04 -5.6014e+03
PROX_CBD -1.4408e+06 -2.7205e+05 -1.7043e+05 -6.6519e+04
PROX_CHILDCARE -3.2769e+06 -2.2120e+05 -5.9600e+04 1.0788e+05
PROX_ELDERLYCARE -1.6966e+06 -3.5677e+04 8.9834e+04 2.0528e+05
PROX_URA_GROWTH_AREA -7.0723e+05 9.2127e+03 7.3288e+04 1.8958e+05
PROX_HAWKER_MARKET -6.4011e+05 -5.6009e+04 8.2983e+04 4.3080e+05
PROX_KINDERGARTEN -1.8548e+06 -3.5646e+05 -1.6452e+05 1.3895e+05
PROX_MRT -2.7453e+06 -6.3607e+05 -2.4833e+05 -7.1555e+04
PROX_PARK -1.0939e+06 -1.5591e+05 1.0240e+04 3.2499e+05
PROX_PRIMARY_SCH -6.2015e+05 -1.8895e+05 1.4868e+03 3.8470e+05
PROX_TOP_PRIMARY_SCH -7.6447e+05 -1.1127e+05 -1.7162e+04 5.2347e+04
PROX_SHOPPING_MALL -9.5378e+05 -1.6176e+05 -2.1287e+04 7.0973e+04
PROX_SUPERMARKET -7.2192e+05 -1.1000e+05 -5.9072e+03 1.5699e+05
PROX_BUS_STOP -6.2152e+05 4.5824e+04 4.3923e+05 1.5814e+06
NO_Of_UNITS -1.6083e+03 -2.9384e+02 -1.0619e+02 5.5081e+00
FAMILY_FRIENDLY -1.4636e+06 -4.4834e+04 1.4704e+04 1.7087e+05
FREEHOLD -1.4458e+05 8.0776e+04 1.8066e+05 3.4671e+05
Max.
Intercept 9517178.8
AREA_SQM 19022.4
AGE 34460.5
PROX_CBD 535635.5
PROX_CHILDCARE 1209089.3
PROX_ELDERLYCARE 2236812.0
PROX_URA_GROWTH_AREA 1511628.7
PROX_HAWKER_MARKET 2415026.9
PROX_KINDERGARTEN 782179.4
PROX_MRT 734567.9
PROX_PARK 1074304.2
PROX_PRIMARY_SCH 1472504.3
PROX_TOP_PRIMARY_SCH 883983.8
PROX_SHOPPING_MALL 595084.1
PROX_SUPERMARKET 1647456.7
PROX_BUS_STOP 5131416.8
NO_Of_UNITS 1483.4
FAMILY_FRIENDLY 1278255.4
FREEHOLD 885791.0
************************Diagnostic information*************************
Number of data points: 1436
Effective number of parameters (2trace(S) - trace(S'S)): 310.8448
Effective degrees of freedom (n-2trace(S) + trace(S'S)): 1125.155
AICc (GWR book, Fotheringham, et al. 2002, p. 61, eq 2.33): 42237.55
AIC (GWR book, Fotheringham, et al. 2002,GWR p. 96, eq. 4.22): 41869.48
BIC (GWR book, Fotheringham, et al. 2002,GWR p. 61, eq. 2.34): 42030.5
Residual sum of squares: 3.238745e+14
R-square value: 0.860678
Adjusted R-square value: 0.8221535
***********************************************************************
Program stops at: 2024-03-11 11:27:58.505342 Adaptive
Show the code
bw.adaptive <- bw.gwr(formula = SELLING_PRICE ~ AREA_SQM + AGE +
PROX_CBD + PROX_CHILDCARE + PROX_ELDERLYCARE +
PROX_URA_GROWTH_AREA + PROX_MRT + PROX_PARK +
PROX_PRIMARY_SCH + PROX_SHOPPING_MALL + PROX_BUS_STOP +
NO_Of_UNITS + FAMILY_FRIENDLY + FREEHOLD,
data=condo_resale.sp,
approach="CV",
kernel="gaussian",
adaptive=TRUE,
longlat=FALSE)Adaptive bandwidth: 895 CV score: 7.952401e+14
Adaptive bandwidth: 561 CV score: 7.667364e+14
Adaptive bandwidth: 354 CV score: 6.953454e+14
Adaptive bandwidth: 226 CV score: 6.15223e+14
Adaptive bandwidth: 147 CV score: 5.674373e+14
Adaptive bandwidth: 98 CV score: 5.426745e+14
Adaptive bandwidth: 68 CV score: 5.168117e+14
Adaptive bandwidth: 49 CV score: 4.859631e+14
Adaptive bandwidth: 37 CV score: 4.646518e+14
Adaptive bandwidth: 30 CV score: 4.422088e+14
Adaptive bandwidth: 25 CV score: 4.430816e+14
Adaptive bandwidth: 32 CV score: 4.505602e+14
Adaptive bandwidth: 27 CV score: 4.462172e+14
Adaptive bandwidth: 30 CV score: 4.422088e+14 Show the code
gwr.adaptive <- gwr.basic(formula = SELLING_PRICE ~ AREA_SQM + AGE +
PROX_CBD + PROX_CHILDCARE + PROX_ELDERLYCARE +
PROX_URA_GROWTH_AREA + PROX_MRT + PROX_PARK +
PROX_PRIMARY_SCH + PROX_SHOPPING_MALL + PROX_BUS_STOP +
NO_Of_UNITS + FAMILY_FRIENDLY + FREEHOLD,
data=condo_resale.sp,
bw=bw.adaptive,
kernel = 'gaussian',
adaptive=TRUE,
longlat = FALSE)
gwr.adaptive ***********************************************************************
* Package GWmodel *
***********************************************************************
Program starts at: 2024-03-11 11:28:10.045516
Call:
gwr.basic(formula = SELLING_PRICE ~ AREA_SQM + AGE + PROX_CBD +
PROX_CHILDCARE + PROX_ELDERLYCARE + PROX_URA_GROWTH_AREA +
PROX_MRT + PROX_PARK + PROX_PRIMARY_SCH + PROX_SHOPPING_MALL +
PROX_BUS_STOP + NO_Of_UNITS + FAMILY_FRIENDLY + FREEHOLD,
data = condo_resale.sp, bw = bw.adaptive, kernel = "gaussian",
adaptive = TRUE, longlat = FALSE)
Dependent (y) variable: SELLING_PRICE
Independent variables: AREA_SQM AGE PROX_CBD PROX_CHILDCARE PROX_ELDERLYCARE PROX_URA_GROWTH_AREA PROX_MRT PROX_PARK PROX_PRIMARY_SCH PROX_SHOPPING_MALL PROX_BUS_STOP NO_Of_UNITS FAMILY_FRIENDLY FREEHOLD
Number of data points: 1436
***********************************************************************
* Results of Global Regression *
***********************************************************************
Call:
lm(formula = formula, data = data)
Residuals:
Min 1Q Median 3Q Max
-3470778 -298119 -23481 248917 12234210
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 527633.22 108183.22 4.877 1.20e-06 ***
AREA_SQM 12777.52 367.48 34.771 < 2e-16 ***
AGE -24687.74 2754.84 -8.962 < 2e-16 ***
PROX_CBD -77131.32 5763.12 -13.384 < 2e-16 ***
PROX_CHILDCARE -318472.75 107959.51 -2.950 0.003231 **
PROX_ELDERLYCARE 185575.62 39901.86 4.651 3.61e-06 ***
PROX_URA_GROWTH_AREA 39163.25 11754.83 3.332 0.000885 ***
PROX_MRT -294745.11 56916.37 -5.179 2.56e-07 ***
PROX_PARK 570504.81 65507.03 8.709 < 2e-16 ***
PROX_PRIMARY_SCH 159856.14 60234.60 2.654 0.008046 **
PROX_SHOPPING_MALL -220947.25 36561.83 -6.043 1.93e-09 ***
PROX_BUS_STOP 682482.22 134513.24 5.074 4.42e-07 ***
NO_Of_UNITS -245.48 87.95 -2.791 0.005321 **
FAMILY_FRIENDLY 146307.58 46893.02 3.120 0.001845 **
FREEHOLD 350599.81 48506.48 7.228 7.98e-13 ***
---Significance stars
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 756000 on 1421 degrees of freedom
Multiple R-squared: 0.6507
Adjusted R-squared: 0.6472
F-statistic: 189.1 on 14 and 1421 DF, p-value: < 2.2e-16
***Extra Diagnostic information
Residual sum of squares: 8.120609e+14
Sigma(hat): 752522.9
AIC: 42966.76
AICc: 42967.14
BIC: 41731.39
***********************************************************************
* Results of Geographically Weighted Regression *
***********************************************************************
*********************Model calibration information*********************
Kernel function: gaussian
Adaptive bandwidth: 30 (number of nearest neighbours)
Regression points: the same locations as observations are used.
Distance metric: Euclidean distance metric is used.
****************Summary of GWR coefficient estimates:******************
Min. 1st Qu. Median 3rd Qu.
Intercept -1.3487e+08 -2.4669e+05 7.7928e+05 1.6194e+06
AREA_SQM 3.3188e+03 5.6285e+03 7.7825e+03 1.2738e+04
AGE -9.6746e+04 -2.9288e+04 -1.4043e+04 -5.6119e+03
PROX_CBD -2.5330e+06 -1.6256e+05 -7.7242e+04 2.6624e+03
PROX_CHILDCARE -1.2790e+06 -2.0175e+05 8.7158e+03 3.7778e+05
PROX_ELDERLYCARE -1.6212e+06 -9.2050e+04 6.1029e+04 2.8184e+05
PROX_URA_GROWTH_AREA -7.2686e+06 -3.0350e+04 4.5869e+04 2.4613e+05
PROX_MRT -4.3781e+07 -6.7282e+05 -2.2115e+05 -7.4593e+04
PROX_PARK -2.9020e+06 -1.6782e+05 1.1601e+05 4.6572e+05
PROX_PRIMARY_SCH -8.6418e+05 -1.6627e+05 -7.7853e+03 4.3222e+05
PROX_SHOPPING_MALL -1.8272e+06 -1.3175e+05 -1.4049e+04 1.3799e+05
PROX_BUS_STOP -2.0579e+06 -7.1461e+04 4.1104e+05 1.2071e+06
NO_Of_UNITS -2.1993e+03 -2.3685e+02 -3.4699e+01 1.1657e+02
FAMILY_FRIENDLY -5.9879e+05 -5.0927e+04 2.6173e+04 2.2481e+05
FREEHOLD -1.6340e+05 4.0765e+04 1.9023e+05 3.7960e+05
Max.
Intercept 18758355
AREA_SQM 23064
AGE 13303
PROX_CBD 11346650
PROX_CHILDCARE 2892127
PROX_ELDERLYCARE 2465671
PROX_URA_GROWTH_AREA 7384059
PROX_MRT 1186242
PROX_PARK 2588497
PROX_PRIMARY_SCH 3381462
PROX_SHOPPING_MALL 38038564
PROX_BUS_STOP 12081592
NO_Of_UNITS 1010
FAMILY_FRIENDLY 2072414
FREEHOLD 1813995
************************Diagnostic information*************************
Number of data points: 1436
Effective number of parameters (2trace(S) - trace(S'S)): 350.3088
Effective degrees of freedom (n-2trace(S) + trace(S'S)): 1085.691
AICc (GWR book, Fotheringham, et al. 2002, p. 61, eq 2.33): 41982.22
AIC (GWR book, Fotheringham, et al. 2002,GWR p. 96, eq. 4.22): 41546.74
BIC (GWR book, Fotheringham, et al. 2002,GWR p. 61, eq. 2.34): 41914.08
Residual sum of squares: 2.528227e+14
R-square value: 0.8912425
Adjusted R-square value: 0.8561185
***********************************************************************
Program stops at: 2024-03-11 11:28:11.685134 The SDF table stores all the model results (Standard Error SE, )
Visualise
tmap mode set to interactive viewingShow the code
Warning: The shape mpsz is invalid (after reprojection). See sf::st_is_validThe sync parameter will synchronise the maps when one of them moved
tmap mode set to interactive viewingShow the code
AREA_SQM_SE <- tm_shape(mpsz)+
tm_polygons(alpha = 0.1) +
tm_shape(condo_resale_sdf.adaptive) +
tm_dots(col = "AREA_SQM_SE",
border.col = "gray60",
border.lwd = 1) +
tm_view(set.zoom.limits = c(11,14))
AREA_SQM_TV <- tm_shape(mpsz)+
tm_polygons(alpha = 0.1) +
tm_shape(condo_resale_sdf.adaptive) +
tm_dots(col = "AREA_SQM_TV",
border.col = "gray60",
border.lwd = 1) +
tm_view(set.zoom.limits = c(11,14))
tmap_arrange(AREA_SQM_SE, AREA_SQM_TV,
asp=1, ncol=2,
sync = TRUE)Warning: The shape mpsz is invalid (after reprojection). See sf::st_is_valid
Warning: The shape mpsz is invalid (after reprojection). See sf::st_is_validtmap mode set to plotting